Hypoxia activates SREBP2 through Golgi disassembly in bone marrow-derived monocytes for enhanced tumor growth.

Bone marrow-derived cells (BMDCs) infiltrate hypoxic tumors at a pre-angiogenic state and differentiate into mature macrophages, thereby inducing pro-tumorigenic immunity. A critical factor regulating this differentiation is activation of SREBP2-a well-known transcription factor participating in tumorigenesis progression-through unknown cellular mechanisms. Here, we show that hypoxia-induced Golgi disassembly and Golgi-ER fusion in monocytic myeloid cells result in nuclear translocation and activation of SREBP2 in a SCAP-independent manner. Notably, hypoxia-induced SREBP2 activation was only observed in an immature lineage of bone marrow-derived cells. Single-cell RNA-seq analysis revealed that SREBP2-mediated cholesterol biosynthesis was upregulated in HSCs and monocytes but not in macrophages in the hypoxic bone marrow niche. Moreover, inhibition of cholesterol biosynthesis impaired tumor growth through suppression of pro-tumorigenic immunity and angiogenesis. Thus, our findings indicate that Golgi-ER fusion regulates SREBP2-mediated metabolic alteration in lineage-specific BMDCs under hypoxia for tumor progression.

UNMF: a unified nonnegative matrix factorization for multi-dimensional omics data.

Factor analysis, ranging from principal component analysis to nonnegative matrix factorization, represents a foremost approach in analyzing multi-dimensional data to extract valuable patterns, and is increasingly being applied in the context of multi-dimensional omics datasets represented in tensor form. However, traditional analytical methods are heavily dependent on the format and structure of the data itself, and if these change even slightly, the analyst must change their data analysis strategy and techniques and spend a considerable amount of time on data preprocessing. Additionally, many traditional methods cannot be applied as-is in the presence of missing values in the data. We present a new statistical framework, unified nonnegative matrix factorization (UNMF), for finding informative patterns in messy biological data sets. UNMF is designed for tidy data format and structure, making data analysis easier and simplifying the development of data analysis tools. UNMF can handle a wide range of data structures and formats, and works seamlessly with tensor data including missing observations and repeated measurements. The usefulness of UNMF is demonstrated through its application to several multi-dimensional omics data, offering user-friendly and unified features for analysis and integration. Its application holds great potential for the life science community. UNMF is implemented with R and is available from GitHub (https://github.com/abikoushi/moltenNMF).

LineageVAE: reconstructing historical cell states and transcriptomes toward unobserved progenitors.

MOTIVATION: Single-cell RNA sequencing (scRNA-seq) enables comprehensive characterization of the cell state. However, its destructive nature prohibits measuring gene expression changes during dynamic processes such as embryogenesis or cell state divergence due to injury or disease. Although recent studies integrating scRNA-seq with lineage tracing have provided clonal insights between progenitor and mature cells, challenges remain. Because of their experimental nature, observations are sparse, and cells observed in the early state are not the exact progenitors of cells observed at later time points. To overcome these limitations, we developed LineageVAE, a novel computational methodology that utilizes deep learning based on the property that cells sharing barcodes have identical progenitors. RESULTS: LineageVAE is a deep generative model that transforms scRNA-seq observations with identical lineage barcodes into sequential trajectories toward a common progenitor in a latent cell state space. This method enables the reconstruction of unobservable cell state transitions, historical transcriptomes, and regulatory dynamics at a single-cell resolution. Applied to hematopoiesis and reprogrammed fibroblast datasets, LineageVAE demonstrated its ability to restore backward cell state transitions and infer progenitor heterogeneity and transcription factor activity along differentiation trajectories. AVAILABILITY AND IMPLEMENTATION: The LineageVAE model was implemented in Python using the PyTorch deep learning library. The code is available on GitHub at https://github.com/LzrRacer/LineageVAE/.

Increased flexibility of the SARS-CoV-2 RNA-binding site causes resistance to remdesivir.

Mutations continue to accumulate within the SARS-CoV-2 genome, and the ongoing epidemic has shown no signs of ending. It is critical to predict problematic mutations that may arise in clinical environments and assess their properties in advance to quickly implement countermeasures against future variant infections. In this study, we identified mutations resistant to remdesivir, which is widely administered to SARS-CoV-2-infected patients, and discuss the cause of resistance. First, we simultaneously constructed eight recombinant viruses carrying the mutations detected in in vitro serial passages of SARS-CoV-2 in the presence of remdesivir. We confirmed that all the mutant viruses didn't gain the virus production efficiency without remdesivir treatment. Time course analyses of cellular virus infections showed significantly higher infectious titers and infection rates in mutant viruses than wild type virus under treatment with remdesivir. Next, we developed a mathematical model in consideration of the changing dynamic of cells infected with mutant viruses with distinct propagation properties and defined that mutations detected in in vitro passages canceled the antiviral activities of remdesivir without raising virus production capacity. Finally, molecular dynamics simulations of the NSP12 protein of SARS-CoV-2 revealed that the molecular vibration around the RNA-binding site was increased by the introduction of mutations on NSP12. Taken together, we identified multiple mutations that affected the flexibility of the RNA binding site and decreased the antiviral activity of remdesivir. Our new insights will contribute to developing further antiviral measures against SARS-CoV-2 infection.

Coordinated demethylation of H3K9 and H3K27 is required for rapid inflammatory responses of endothelial cells.

Histone H3 lysine-9 di-methylation (H3K9me2) and lysine-27 tri-methylation (H3K27me3) are linked to repression of gene expression, but the functions of repressive histone methylation dynamics during inflammatory responses remain enigmatic. Here, we report that lysine demethylases 7A (KDM7A) and 6A (UTX) play crucial roles in tumor necrosis factor (TNF)-α signaling in endothelial cells (ECs), where they are regulated by a novel TNF-α-responsive microRNA, miR-3679-5p. TNF-α rapidly induces co-occupancy of KDM7A and UTX at nuclear factor kappa-B (NF-κB)-associated elements in human ECs. KDM7A and UTX demethylate H3K9me2 and H3K27me3, respectively, and are both required for activation of NF-κB-dependent inflammatory genes. Chromosome conformation capture-based methods furthermore uncover increased interactions between TNF-α-induced super enhancers at NF-κB-relevant loci, coinciding with KDM7A and UTX recruitments. Simultaneous pharmacological inhibition of KDM7A and UTX significantly reduces leukocyte adhesion in mice, establishing the biological and potential translational relevance of this mechanism. Collectively, these findings suggest that rapid erasure of repressive histone marks by KDM7A and UTX is essential for NF-κB-dependent regulation of genes that control inflammatory responses of ECs.

Convergent genomic diversity and novel BCAA metabolism in intrahepatic cholangiocarcinoma.

BACKGROUND: Driver alterations may represent novel candidates for driver gene-guided therapy; however, intrahepatic cholangiocarcinoma (ICC) with multiple genomic aberrations makes them intractable. Therefore, the pathogenesis and metabolic changes of ICC need to be understood to develop new treatment strategies. We aimed to unravel the evolution of ICC and identify ICC-specific metabolic characteristics to investigate the metabolic pathway associated with ICC development using multiregional sampling to encompass the intra- and inter-tumoral heterogeneity. METHODS: We performed the genomic, transcriptomic, proteomic and metabolomic analysis of 39-77 ICC tumour samples and eleven normal samples. Further, we analysed their cell proliferation and viability. RESULTS: We demonstrated that intra-tumoral heterogeneity of ICCs with distinct driver genes per case exhibited neutral evolution, regardless of their tumour stage. Upregulation of BCAT1 and BCAT2 indicated the involvement of 'Val Leu Ile degradation pathway'. ICCs exhibit the accumulation of ubiquitous metabolites, such as branched-chain amino acids including valine, leucine, and isoleucine, to negatively affect cancer prognosis. We revealed that this metabolic pathway was almost ubiquitously altered in all cases with genomic diversity and might play important roles in tumour progression and overall survival. CONCLUSIONS: We propose a novel ICC onco-metabolic pathway that could enable the development of new therapeutic interventions.

Divergent lncRNA MYMLR regulates MYC by eliciting DNA looping and promoter-enhancer interaction.

Long non-coding RNAs (lncRNAs) function in a wide range of processes by diverse mechanisms, though their roles in regulation of oncogenes and/or tumor suppressors remain rather elusive. We performed a global search for lncRNAs affecting MYC activity using a systems biology-based approach with a K supercomputer and the GIMLET algorism based on local distance correlations. Consequently, MYMLR was identified and experimentally shown to maintain MYC transcriptional activity and cell cycle progression despite the low levels of expression. A proteomic search for MYMLR-binding proteins identified PCBP2, while it was also found that MYMLR places a 557-kb upstream enhancer region in the proximity of the MYC promoter in cooperation with PCBP2. These findings implicate a crucial role for MYMLR in regulation of the archetypical oncogene MYC and warrant future studies regarding the involvement of low copy number lncRNAs in regulation of other crucial oncogenes and tumor suppressor genes.

Smoking enhances the expression of angiotensin-converting enzyme 2 involved in the efficiency of severe acute respiratory syndrome coronavirus 2 infection.

Smoking is one of the risk factors most closely related to the severity of coronavirus disease 2019 (COVID-19). However, the relationship between smoking history and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infectivity is unknown. In this study, we evaluated the ACE2 expression level in the lungs of current smokers, ex-smokers, and nonsmokers. The ACE2 expression level of ex-smokers who smoked cigarettes until recently (cessation period shorter than 6 months) was higher than that of nonsmokers and ex-smokers with a long history of nonsmoking (cessation period longer than 6 months). We also showed that the efficiency of SARS-CoV-2 infection was enhanced in a manner dependent on the angiotensin-converting enzyme 2 (ACE2) expression level. Using RNA-seq analysis on the lungs of smokers, we identified that the expression of inflammatory signaling genes was correlated with ACE2 expression. Notably, with increasing duration of smoking cessation among ex-smokers, not only ACE2 expression level but also the expression levels of inflammatory signaling genes decreased. These results indicated that smoking enhances the expression levels of ACE2 and inflammatory signaling genes. Our data suggest that the efficiency of SARS-CoV-2 infection is enhanced by smoking-mediated upregulation of ACE2 expression level.

CYBERTRACK2.0: zero-inflated model-based cell clustering and population tracking method for longitudinal mass cytometry data.

SUMMARY: Recent advancements in high-dimensional single-cell technologies, such as mass cytometry, enable longitudinal experiments to track dynamics of cell populations and identify change points where the proportions vary significantly. However, current research is limited by the lack of tools specialized for analyzing longitudinal mass cytometry data. In order to infer cell population dynamics from such data, we developed a statistical framework named CYBERTRACK2.0. The framework's analytic performance was validated against synthetic and real data, showing that its results are consistent with previous research. AVAILABILITY AND IMPLEMENTATION: CYBERTRACK2.0 is available at https://github.com/kodaim1115/CYBERTRACK2. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Hierarchical non-negative matrix factorization using clinical information for microbial communities.

BACKGROUND: The human microbiome forms very complex communities that consist of hundreds to thousands of different microorganisms that not only affect the host, but also participate in disease processes. Several state-of-the-art methods have been proposed for learning the structure of microbial communities and to investigate the relationship between microorganisms and host environmental factors. However, these methods were mainly designed to model and analyze single microbial communities that do not interact with or depend on other communities. Such methods therefore cannot comprehend the properties between interdependent systems in communities that affect host behavior and disease processes. RESULTS: We introduce a novel hierarchical Bayesian framework, called BALSAMICO (BAyesian Latent Semantic Analysis of MIcrobial COmmunities), which uses microbial metagenome data to discover the underlying microbial community structures and the associations between microbiota and their environmental factors. BALSAMICO models mixtures of communities in the framework of nonnegative matrix factorization, taking into account environmental factors. We proposes an efficient procedure for estimating parameters. A simulation then evaluates the accuracy of the estimated parameters. Finally, the method is used to analyze clinical data. In this analysis, we successfully detected bacteria related to colorectal cancer. CONCLUSIONS: These results show that the method not only accurately estimates the parameters needed to analyze the connections between communities of microbiota and their environments, but also allows for the effective detection of these communities in real-world circumstances.

Model-based clustering for flow and mass cytometry data with clinical information.

BACKGROUND: High-dimensional flow cytometry and mass cytometry allow systemic-level characterization of more than 10 protein profiles at single-cell resolution and provide a much broader landscape in many biological applications, such as disease diagnosis and prediction of clinical outcome. When associating clinical information with cytometry data, traditional approaches require two distinct steps for identification of cell populations and statistical test to determine whether the difference between two population proportions is significant. These two-step approaches can lead to information loss and analysis bias. RESULTS: We propose a novel statistical framework, called LAMBDA (Latent Allocation Model with Bayesian Data Analysis), for simultaneous identification of unknown cell populations and discovery of associations between these populations and clinical information. LAMBDA uses specified probabilistic models designed for modeling the different distribution information for flow or mass cytometry data, respectively. We use a zero-inflated distribution for the mass cytometry data based the characteristics of the data. A simulation study confirms the usefulness of this model by evaluating the accuracy of the estimated parameters. We also demonstrate that LAMBDA can identify associations between cell populations and their clinical outcomes by analyzing real data. LAMBDA is implemented in R and is available from GitHub ( https://github.com/abikoushi/lambda ).

A network of networks approach for modeling interconnected brain tissue-specific networks.

MOTIVATION: Recent sequence-based analyses have identified a lot of gene variants that may contribute to neurogenetic disorders such as autism spectrum disorder and schizophrenia. Several state-of-the-art network-based analyses have been proposed for mechanical understanding of genetic variants in neurogenetic disorders. However, these methods were mainly designed for modeling and analyzing single networks that do not interact with or depend on other networks, and thus cannot capture the properties between interdependent systems in brain-specific tissues, circuits and regions which are connected each other and affect behavior and cognitive processes. RESULTS: We introduce a novel and efficient framework, called a 'Network of Networks' approach, to infer the interconnectivity structure between multiple networks where the response and the predictor variables are topological information matrices of given networks. We also propose Graph-Oriented SParsE Learning, a new sparse structural learning algorithm for network data to identify a subset of the topological information matrices of the predictors related to the response. We demonstrate on simulated data that propose Graph-Oriented SParsE Learning outperforms existing kernel-based algorithms in terms of F-measure. On real data from human brain region-specific functional networks associated with the autism risk genes, we show that the 'Network of Networks' model provides insights on the autism-associated interconnectivity structure between functional interaction networks and a comprehensive understanding of the genetic basis of autism across diverse regions of the brain. AVAILABILITY AND IMPLEMENTATION: Our software is available from https://github.com/infinite-point/GOSPEL. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Meta-Analysis of Gut Dysbiosis in Parkinson's Disease.

BACKGROUND: PD may begin with the intestinal accumulation of α-synuclein fibrils, which can be causally associated with gut dysbiosis. The variability of gut microbiota across countries prevented us from identifying shared gut dysbiosis in PD. OBJECTIVES: To identify gut dysbiosis in PD across countries. METHODS: We performed 16S ribosomal RNA gene sequencing analysis of gut microbiota in 223 patients with PD and 137 controls, and meta-analyzed gut dysbiosis by combining our dataset with four previously reported data sets from the United States, Finland, Russia, and Germany. We excluded uncommon taxa from our analyses. For pathway analysis, we developed the Kyoto Encyclopedia of Genes and Genomes orthology set enrichment analysis method. RESULTS: After adjusting for confounding factors (body mass index, constipation, sex, age, and catechol-O-methyl transferase inhibitor), genera Akkermansia and Catabacter, as well as families Akkermansiaceae, were increased, whereas genera Roseburia, Faecalibacterium, and Lachnospiraceae ND3007 group were decreased in PD. Catechol-O-methyl transferase inhibitor intake markedly increased family Lactobacillaceae. Inspection of these bacteria in 12 datasets that were not included in the meta-analysis revealed that increased genus Akkermansia and decreased genera Roseburia and Faecalibacterium were frequently observed across countries. Kyoto Encyclopedia of Genes and Genomes orthology set enrichment analysis revealed changes in short-chain fatty acid metabolisms in our dataset. CONCLUSIONS: We report that intestinal mucin layer-degrading Akkermansia is increased and that short-chain fatty acid-producing Roseburia and Faecalibacterium are decreased in PD across countries. © 2020 International Parkinson and Movement Disorder Society.

Correction: Integrated Multiregional Analysis Proposing a New Model of Colorectal Cancer Evolution.

[This corrects the article DOI: 10.1371/journal.pgen.1005778.].

Model-based cell clustering and population tracking for time-series flow cytometry data.

BACKGROUND: Modern flow cytometry technology has enabled the simultaneous analysis of multiple cell markers at the single-cell level, and it is widely used in a broad field of research. The detection of cell populations in flow cytometry data has long been dependent on "manual gating" by visual inspection. Recently, numerous software have been developed for automatic, computationally guided detection of cell populations; however, they are not designed for time-series flow cytometry data. Time-series flow cytometry data are indispensable for investigating the dynamics of cell populations that could not be elucidated by static time-point analysis. Therefore, there is a great need for tools to systematically analyze time-series flow cytometry data. RESULTS: We propose a simple and efficient statistical framework, named CYBERTRACK (CYtometry-Based Estimation and Reasoning for TRACKing cell populations), to perform clustering and cell population tracking for time-series flow cytometry data. CYBERTRACK assumes that flow cytometry data are generated from a multivariate Gaussian mixture distribution with its mixture proportion at the current time dependent on that at a previous timepoint. Using simulation data, we evaluate the performance of CYBERTRACK when estimating parameters for a multivariate Gaussian mixture distribution, tracking time-dependent transitions of mixture proportions, and detecting change-points in the overall mixture proportion. The CYBERTRACK performance is validated using two real flow cytometry datasets, which demonstrate that the population dynamics detected by CYBERTRACK are consistent with our prior knowledge of lymphocyte behavior. CONCLUSIONS: Our results indicate that CYBERTRACK offers better understandings of time-dependent cell population dynamics to cytometry users by systematically analyzing time-series flow cytometry data.

ENIGMA: an enterotype-like unigram mixture model for microbial association analysis.

BACKGROUND: One of the major challenges in microbial studies is detecting associations between microbial communities and a specific disease. A specialized feature of microbiome count data is that intestinal bacterial communities form clusters called as "enterotype", which are characterized by differences in specific bacterial taxa, making it difficult to analyze these data under health and disease conditions. Traditional probabilistic modeling cannot distinguish between the bacterial differences derived from enterotype and those related to a specific disease. RESULTS: We propose a new probabilistic model, named as ENIGMA (Enterotype-like uNIGram mixture model for Microbial Association analysis), which can be used to address these problems. ENIGMA enabled simultaneous estimation of enterotype-like clusters characterized by the abundances of signature bacterial genera and the parameters of environmental effects associated with the disease. CONCLUSION: In the simulation study, we evaluated the accuracy of parameter estimation. Furthermore, by analyzing the real-world data, we detected the bacteria related to Parkinson's disease. ENIGMA is implemented in R and is available from GitHub ( https://github.com/abikoushi/enigma ).

Dynamically and epigenetically coordinated GATA/ETS/SOX transcription factor expression is indispensable for endothelial cell differentiation.

Although studies of the differentiation from mouse embryonic stem (ES) cells to vascular endothelial cells (ECs) provide an excellent model for investigating the molecular mechanisms underlying vascular development, temporal dynamics of gene expression and chromatin modifications have not been well studied. Herein, using transcriptomic and epigenomic analyses based on H3K4me3 and H3K27me3 modifications at a genome-wide scale, we analysed the EC differentiation steps from ES cells and crucial epigenetic modifications unique to ECs. We determined that Gata2, Fli1, Sox7 and Sox18 are master regulators of EC that are induced following expression of the haemangioblast commitment pioneer factor, Etv2. These master regulator gene loci were repressed by H3K27me3 throughout the mesoderm period but rapidly transitioned to histone modification switching from H3K27me3 to H3K4me3 after treatment with vascular endothelial growth factor. SiRNA knockdown experiments indicated that these regulators are indispensable not only for proper EC differentiation but also for blocking the commitment to other closely aligned lineages. Collectively, our detailed epigenetic analysis may provide an advanced model for understanding temporal regulation of chromatin signatures and resulting gene expression profiles during EC commitment. These studies may inform the future development of methods to stimulate the vascular endothelium for regenerative medicine.

Integrated Multiregional Analysis Proposing a New Model of Colorectal Cancer Evolution.

Understanding intratumor heterogeneity is clinically important because it could cause therapeutic failure by fostering evolutionary adaptation. To this end, we profiled the genome and epigenome in multiple regions within each of nine colorectal tumors. Extensive intertumor heterogeneity is observed, from which we inferred the evolutionary history of the tumors. First, clonally shared alterations appeared, in which C>T transitions at CpG site and CpG island hypermethylation were relatively enriched. Correlation between mutation counts and patients' ages suggests that the early-acquired alterations resulted from aging. In the late phase, a parental clone was branched into numerous subclones. Known driver alterations were observed frequently in the early-acquired alterations, but rarely in the late-acquired alterations. Consistently, our computational simulation of the branching evolution suggests that extensive intratumor heterogeneity could be generated by neutral evolution. Collectively, we propose a new model of colorectal cancer evolution, which is useful for understanding and confronting this heterogeneous disease.

A latent allocation model for the analysis of microbial composition and disease.

BACKGROUND: Establishing the relationship between microbiota and specific diseases is important but requires appropriate statistical methodology. A specialized feature of microbiome count data is the presence of a large number of zeros, which makes it difficult to analyze in case-control studies. Most existing approaches either add a small number called a pseudo-count or use probability models such as the multinomial and Dirichlet-multinomial distributions to explain the excess zero counts, which may produce unnecessary biases and impose a correlation structure taht is unsuitable for microbiome data. RESULTS: The purpose of this article is to develop a new probabilistic model, called BERnoulli and MUltinomial Distribution-based latent Allocation (BERMUDA), to address these problems. BERMUDA enables us to describe the differences in bacteria composition and a certain disease among samples. We also provide a simple and efficient learning procedure for the proposed model using an annealing EM algorithm. CONCLUSION: We illustrate the performance of the proposed method both through both the simulation and real data analysis. BERMUDA is implemented with R and is available from GitHub ( https://github.com/abikoushi/Bermuda ).

phyC: Clustering cancer evolutionary trees.

Multi-regional sequencing provides new opportunities to investigate genetic heterogeneity within or between common tumors from an evolutionary perspective. Several state-of-the-art methods have been proposed for reconstructing cancer evolutionary trees based on multi-regional sequencing data to develop models of cancer evolution. However, there have been few studies on comparisons of a set of cancer evolutionary trees. We propose a clustering method (phyC) for cancer evolutionary trees, in which sub-groups of the trees are identified based on topology and edge length attributes. For interpretation, we also propose a method for evaluating the sub-clonal diversity of trees in the clusters, which provides insight into the acceleration of sub-clonal expansion. Simulation showed that the proposed method can detect true clusters with sufficient accuracy. Application of the method to actual multi-regional sequencing data of clear cell renal carcinoma and non-small cell lung cancer allowed for the detection of clusters related to cancer type or phenotype. phyC is implemented with R(≥3.2.2) and is available from https://github.com/ymatts/phyC.